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A database for the epidemic trends and control measures during the first wave of COVID-19 in mainland China

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Title: A database for the epidemic trends and control measures during the first wave of COVID-19 in mainland China
Authors: Fu, H
Wang, H
Xi, X
Boonyasiri, A
Wang, Y
Hinsley, W
Fraser, KJ
McCabe, R
Olivera Mesa, D
Skarp, J
Ledda, A
Dewé, T
Dighe, A
Winskill, P
Van Elsland, SL
Ainslie, KEC
Baguelin, M
Bhatt, S
Boyd, O
Brazeau, NF
Cattarino, L
Charles, G
Coupland, H
Cucunubá, ZM
Cuomo-Dannenburg, G
Donnelly, CA
Dorigatti, I
Eales, OD
Fitzjohn, RG
Flaxman, S
Gaythorpe, KAM
Ghani, AC
Green, WD
Hamlet, A
Hauck, K
Haw, DJ
Jeffrey, B
Laydon, DJ
Lees, JA
Mellan, T
Mishra, S
Nedjati Gilani, G
Nouvellet, P
Okell, L
Parag, KV
Ragonnet-Cronin, M
Riley, S
Schmit, N
Thompson, HA
Unwin, HJT
Verity, R
Vollmer, MAC
Volz, E
Walker, PGT
Walters, CE
Waston, OJ
Whittaker, C
Whittles, LK
Imai, N
Bhatia, S
Ferguson, NM
Item Type: Journal Article
Abstract: Objectives: This data collation effort aims to provide a comprehensive database to describe the epidemic trends and responses during the first wave of coronavirus disease 2019 (COVID-19)across main provinces in China. Methods: From mid-January to March 2020, we extracted publicly available data on the spread and control of COVID-19 from 31 provincial health authorities and major media outlets in mainland China. Based on these data, we conducted a descriptive analysis of the epidemics in the six most-affected provinces. Results: School closures, travel restrictions, community-level lockdown, and contact tracing were introduced concurrently around late January but subsequent epidemic trends were different across provinces. Compared to Hubei, the other five most-affected provinces reported a lower crude case fatality ratio and proportion of critical and severe hospitalised cases. From March 2020, as local transmission of COVID-19 declined, switching the focus of measures to testing and quarantine of inbound travellers could help to sustain the control of the epidemic. Conclusions: Aggregated indicators of case notifications and severity distributions are essential for monitoring an epidemic. A publicly available database with these indicators and information on control measures provides useful source for exploring further research and policy planning for response to the COVID-19 epidemic.
Issue Date: Jan-2021
Date of Acceptance: 23-Oct-2020
URI: http://hdl.handle.net/10044/1/83522
DOI: 10.1016/j.ijid.2020.10.075
ISSN: 1201-9712
Publisher: Elsevier
Start Page: 463
End Page: 471
Journal / Book Title: International Journal of Infectious Diseases
Volume: 102
Copyright Statement: © 2020 The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Sponsor/Funder: Medical Research Council (MRC)
Abdul Latif Jameel Foundation
Medical Research Council
Wellcome Trust
The Royal Society
Medical Research Council
Medical Research Council (MRC)
Bill & Melinda Gates Foundation
Imperial College Healthcare NHS Trust- BRC Funding
The Academy of Medical Sciences
Bill & Melinda Gates Foundation
National Institute for Health Research
UK Research and Innovation
Medical Research Council (MRC)
Funder's Grant Number: MR/R015600/1
MR/R015600/1
213494/Z/18/Z
DH140134
MR/R015600/1
MR/K010174/1B
1606H5002/JH6
RDA02
SBF004/1080
RES- -62388
NIHR200908
MR/V038109/1
EP/V520354/1
Keywords: Science & Technology
Life Sciences & Biomedicine
Infectious Diseases
COVID-19
China
Epidemic
Control measure
Case fatality ratio
Contact
COVID-19
Case fatality ratio
China
Contact
Control measure
Epidemic
COVID-19
China
Contact Tracing
Databases, Factual
Humans
SARS-CoV-2
Humans
Contact Tracing
Databases, Factual
China
COVID-19
SARS-CoV-2
Microbiology
0605 Microbiology
1108 Medical Microbiology
1117 Public Health and Health Services
Publication Status: Published
Article Number: THEIJID-D-20-03362R1
Online Publication Date: 2020-10-31
Appears in Collections:Mathematics
Department of Infectious Diseases
Statistics
Faculty of Medicine
Imperial College London COVID-19
School of Public Health
Faculty of Natural Sciences



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